Application of Machine Learning in Ethnomusicology Education under the Belt and Road Initiative

Abstract

The integration of machine learning has transformed the landscape of music education. To address the challenges of assessment accuracy and personalized instruction in ethnic music education along the "Belt and Road Initiative" regions, a sophisticated teaching recommendation system was developed, combining collaborative filtering with advanced clustering techniques. This framework demonstrates adaptability across diverse cultural contexts while ensuring pedagogical effectiveness in varied learning settings. The system's recommendation engine synthesizes explicit user feedback with implicit behavioral metrics to generate a holistic profile of student preferences across cultural boundaries. By integrating Canopy and K-Means clustering algorithms through an optimized fusion approach, the system achieves precise modeling of musical learning preferences. This two-tiered clustering strategy optimizes computational efficiency while maintaining recommendation quality, particularly beneficial in settings with limited technological resources. The platform continuously calibrates feature importance based on cultural relevance and educational goals, ensuring culturally sensitive and pedagogically effective recommendations. Empirical testing demonstrates the enhanced algorithm's superior performance in precision, recall, and scope compared to conventional approaches. The system achieves a parallel processing efficiency ratio of 3.326 across four nodes, showing robust scalability for extensive educational applications. This innovation facilitates personalized learning experiences in ethnic music education throughout the Belt and Road nations while fostering cultural preservation and exchange. The system successfully bridges traditional musical heritage with contemporary educational techniques, supporting both conservation and advancement in ethnic music pedagogy.